Material decomposition of multi-spectral x-ray projections using neural networks

ABSTRACT

A method of processing x-ray images comprises training an artificial neural network to process multi-spectral x-ray projections to determine composition information about an object in terms of equivalent thickness of at least one basis material. The method further comprises providing a multi-spectral x-ray projection of an object, wherein the multi-spectral x-ray projection of the object contains energy content information describing the energy content of the multi-spectral x-ray projection, The multi-spectral x-ray projection is then processed with the artificial neural network to determine composition information about the object, and then the composition information about the object is provided

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under EB015094-01A1awarded by NIH. The government has certain rights in the invention

BACKGROUND

X-ray transmission imaging systems, such as projection imaging,tomosynthesis, and computed tomography (CT), create images of materialsbased on their density and energy-dependent x-ray attenuationproperties. Typical x-ray imaging systems use an x-ray beam with apolyenergetic x-ray spectrum. However, conventional x-ray imagingsystems do not collect information about the energy content of thedetected signal across multiple spectra, meaning that information aboutthe material-specific energy-dependent signature of an image object islost. Because of this lost information, materials with differentcompositions may be indistinguishable in the resulting x-ray image. Forexample, two different types of kidney stones may look the same on aregular CT scan despite the fact that the kidney stones are comprised ofdifferent materials. In another example, iodine (which is injected toprovide contrast to the blood) can sometimes be indistinguishable in aregular CT scan to a calcified plaque in a blood vessel despite the factthat the iodine and the calcified plaque have different compositions.

New imaging techniques are being developed to collect multi-spectraldata, which can be utilized to determine information about materialcomposition of an imaged object. One currently-available method ofprocessing measured multi-spectral x-ray data to extract informationabout material composition of an object is to decompose themulti-spectral data into basis functions, resulting in a set of basiscoefficients for each measurement. A mathematical relationship betweenthe basis coefficients and the multi-spectral x-ray data is known forthe case of ideal systems and can be solved numerically using existingalgorithms. This method is complicated by the fact that, in practice,the x-ray imaging process is affected by physical nonidealities thatmake it difficult to solve for the basis coefficients with existingmethods. Material decomposition can be performed by solving a set oflinear equations for mono-energetic x-ray transmissions assuming perfectdetectors.

The problem becomes even more difficult and nonlinear when using astandard x-ray beam consisting of varying energy photons and whenconsidering the effects of the non-ideal detector. Existing technologiesfor material decomposition from multi-spectral x-ray data includecompensatory algorithms for non-ideal detector responses based on thelaws governing physical processes. Models of the detector's energyresponse and pulse pileup must be used. These models are parameterizedbased on the specific brand and type of the detector and may requireradioactive isotopes for determining the parameters. The gold standardis the maximum likelihood estimator, which is iterative in nature andperforms poorly without prior knowledge of system parameters. Inaddition, empirical methods exist using calibration data to createcorrection look-up tables. These algorithms may require significantprocessing power or computer memory, require prior knowledge of thesystem, and may not sufficiently model the underlying phenomenaentirely, leading to less accurate results.

SUMMARY

The present inventors recognize that a method and system are needed forprocessing x-ray images to accurately determine information aboutmaterial composition of an imaged object without prior knowledge of thesystem and without the need for solving complex nonlinear equations andusing look-up tables to correct for nonidealities in the system.Further, the disclosed method and system has the benefit of being morememory efficient than other comparable empirical methods of determiningmaterial composition of an object, such as the Alvarez method.

In one embodiment, a method of processing x-ray images comprisestraining an artificial neural network to process multi-spectral x-rayprojections to determine composition information in terms of equivalentthickness of at least one basis material. The method further comprisesproviding a multi-spectral x-ray projection of an object, wherein themulti-spectral x-ray projection of the object contains energy contentinformation describing the energy content of the multi-spectral x-rayprojection. The multi-spectral x-ray projection is then processed withthe artificial neural network to determine composition information aboutthe object, and then the composition information about the object isprovided.

Another embodiment of a method of imaging an object comprises projectingmulti-spectral x-ray beams through an object and detecting amulti-spectral x-ray projection through the object, wherein thedetecting includes detecting the energy content of the multi-spectralx-ray beams. The multi-spectral x-ray projection is then processed withan artificial neural network to determine composition information aboutthe object in terms of equivalent thickness of at least one basismaterial.

One embodiment of a system for imaging an object comprises an imagingdevice configured to produce a multi-spectral x-ray image of an objectand an artificial neural network trained to process the multi-spectralx-ray image to produce composition information. The neural network istrained by providing a set of calibration x-ray projections of knownthickness of at least one basis material as input to the neural networkand utilizing a training algorithm to minimize the error between thecomposition information and the known thicknesses. The compositioninformation includes an array of basis coefficients mapping the objectin terms of equivalent thicknesses of the at least one basis material.

Various other features, objects and advantages of the invention will emade apparent from the following description taken together with thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate the best mode presently contemplated of carryingout the disclosure. In the drawings:

FIG. 1 depicts one embodiment of a method of processing x-rayprojections of an object to determine information about materialcomposition of the object.

FIG. 2 depicts one embodiment of a method of imaging an object todetermine information about material composition of the object.

FIG. 3 depicts one embodiment of a training module for a neural networkwhich may process multi-spectral x-ray data to determine informationabout material composition of an object.

FIG. 4 depicts one embodiment of a system for imaging an object todetermine information about material composition of the object.

FIG. 5 depicts one embodiment of an output of a system and method fordetermining information about material composition of the object.

FIG. 6 depicts is a system diagram of an embodiment o a system forprocessing x-ray images.

DETAILED DESCRIPTION OF THE DRAWINGS

FIGS. 1-4 depict embodiments of a method and system for decomposing oneor more multi-spectral x-ray projections 5 of an object 50 to determinecomposition information about the object 50. The method and systemutilize an artificial neural network (ANN) 7 to process themulti-spectral x-ray projection 5 and produce composition information 9,i.e., information about the composition of the imaged object. The ANN 7is trained using a training calibration procedure involving inputtingcalibration projections 20 of various thicknesses of one or morecalibration materials into the ANN 7 and utilizing a training algorithm17 to minimize error in the output of the ANN 7 system.

The multi-spectral x-ray projection 5 is a collection of x-ray dataacross one or more different energy spectra. The multi-spectral x-rayprojections 5 may originate from any type of x-ray imaging device and/ordetector capable of creating such multi-spectral data. The compositioninformation 9 may be any information about or related to the materialcomposition of an imaged object 50. In one embodiment, the compositioninformation 9 may be presented in the form of basis coefficients 45describing the object 50 in terms of equivalent thickness of at leastone basis material. In such an embodiment, the ANN 7 is trained todecompose the multi-spectral x-ray projection 5 into an equivalentamount of a basis material, or an effective combination of N number ofbasis materials, that would provide the same x-ray projectionmeasurement. To provide an illustrative example, a trained ANN 7processing a multi-spectral x-ray projection 5 of one centimeter ofTeflon may produce composition information 9 describing that object interms of a specified thickness of water (e.g., x cm of H₂O) that wouldproduce the same x-ray attenuation as exhibited in the x-ray projectionof the one centimeter of Teflon.

Similarly, the ANN 7 may be trained to produce composition information 9in terms of equivalent thicknesses of two or more materials. Referringagain to the previous example, the ANN 7 may produce compositioninformation 9 for a projection 5 of one centimeter of Teflon as aneffective combination of thicknesses of two or more basis materials,such as a specified thickness of aluminum in combination with aspecified thickness of polymethyl methacrylate (PMMA). The basiscoefficients 49 may be a signature for each material, which is how thecoefficients may be used to identify the material composition of anobject. The composition information 9 may further include otherinformation about the composition of an object. For example, the basiscoefficients 49 may be used to determine effective atomic number andeffective density for an object.

The composition information 9 may be in any form capable of conveyinginformation about the composition of an object 50, including in the formof a spatial map of the object 50 or an array that correlates to alocation on an x-ray projection or reconstructed CT image slice. Forexample, basis coefficients 49 may be generated for each pixel in animage, which may be a two dimensional array of coefficients or a threedimensional array of coefficients that correspond to a two dimensionalor three dimensional image, respectively. Likewise, the compositioninformation 9 may comprise a two dimensional or three dimensional arrayof effective atomic numbers and effective density for all pixels in atwo dimensional or three dimensional image. Alternatively oradditionally, the effective atomic numbers and effective densities maybe determined by another processing unit (such as a CPU) based oncomposition information 9 produced by the ANN 7.

As depicted in FIG. 1, for example, a multi-spectral x-ray projection 5may be fed to an artificial neural network 7. The ANN 7 is trained bytraining module 15 to process energy content of the multi-spectral x-rayprojection 5 to determine particular composition information about animaged object. For example, the ANN 7 may be trained to determinecomposition information in terms of equivalent thickness of one or morespecified basis materials. The ANN 7 produces the compositioninformation 9 pursuant to its training, The composition information 9may be processed at step 11, for example, by a centralized processingunit or other computer processor connected directly or indirectly to theANN 7. The composition information 9 may be processed to produce anobject composition output 13, such as a visual depiction or writtendescription of the object's composition.

The object composition output 13 may be any output that providesinformation to a user regarding the material composition of an object.For example, the object composition output 13 may be a graph (such asFIG. 5) demonstrating the basis coefficients 49, or the equivalentthickness of each basis material. In another embodiment, the objectcomposition output 13 may be a map or an image describing or depictingthe material composition of an object in the form of x-ray projections.In still other embodiments, the object composition output 13 may be awritten description of a material composition of the image object. Infurther embodiments, the object composition output 13 may be maps orimages describing or depicting the material composition generated by aCT reconstruction algorithm.

As shown in better detail in FIGS. 2 and 3, the training module 15conducts the training of the ANN 7 according to a training algorithm 17.The training module 16 trains the ANN 7 to produce desired compositioninformation 9 based on multi-spectral x-ray projections 5. In theexemplary embodiment depicted in FIG. 2, the training module 15 hastraining algorithms 17 designed to train ANN 7 to produce basiscoefficients in terms of equivalent thicknesses of a combination ofthree materials—namely, PMMA, aluminum, and iodine. Calibrationprojections 20A-C are input into or otherwise made a part of thetraining algorithm 17. In FIG. 2, calibration projections for PMMA 20A,aluminum 2013, and iodine 20C are given to or are accessible by thetraining algorithm 17. The calibration projections 20A-20C may comprisemulti-spectra x-ray projections of various thicknesses and combinationsof the respective materials, PMMA, aluminum, and iodine. Eachcalibration projection 20A-20C may further comprise information aboutthe projection image and/or the imaged calibration object, such as theknown basis material and the thickness of the known basis material.Furthermore, other information about the calibration images 20A-20C maybe given to the training algorithm 17, such as information about theimaging device used to produce the calibration projection 20A-20C, theimaging method, and the energy spectrum for the multi-spectral x-rayused. Likewise, the same may be true for multi-spectral x-rayprojections 5 input into the trained ANN 7.

FIG. 3 depicts an embodiment of a training module 15 in more detail.Calibration projections 20 and training information 22, such as thatdescribed above, are given to or are accessible by to the trainingalgorithm 17. The training algorithm 17 feeds the calibration images orselect portions thereof to the ANN 7. The ANN 7 generates compositioninformation 9 based on the calibration images 20, which is sent to thecomparator 24. The training algorithm 17 feeds the training information22 associated with the calibration images 20 to the comparator 24, whichthen compares the received composition information 9 to the traininginformation 22 associated with the one or more calibration images 20.The comparator determines whether or not the composition information 9generated by the ANN 7 is close enough to the training information 22 toreach a desired or predetermined threshold level of accuracy. At step26, it is determined whether the composition information 9 issufficiently accurate—i.e. whether the error has been sufficientlyminimized such that the ANN 7 can function to accurately generatecomposition information 9 about imaged objects, such as those in the oneor more calibration images 20. At step 26, if the error has beenminimized then the training is terminated at step 28 and the neuralnetwork may be implemented to process multi-spectral x-ray projectiondata. If, on the other hand, the error between the compositioninformation 9 and the training information 22 is too great, then thetraining algorithm 17 conducts further training of the ANN 7 usingadditional calibration images 20. The process is thus repeated until theerror is minimized,

Once the ANN 7 has been sufficiently trained by the training algorithm15, it may be implemented to process multi-spectral x-ray projections 5.For example, returning to FIG. 2, the ANN 7 is trained and employed toproduce basis coefficients 49 in terms of equivalent thicknesses ofPMMA, aluminum, and iodine. For example, in the depicted method andsystem for imaging an object, multi-spectral x-rays are projected 36through an object 50, The multi-spectral x-rays are then detected 37.Such multi-spectral x-ray detection includes the collection ofmulti-spectral data, or spectral information, about the x-rays detectedthrough the object 50. That information is then fed to the ANN 7 whichwill produce basis coefficients 49 for the object.

The basis coefficients 49 may then be further processed, for example bya CPU 33 (FIG. 4), to provide further processing and output, for exampleto a user. As shown in FIG. 4, a system for imaging an object maycomprise an imaging device 31 connected to a central processing unit 33.The central processing unit 33 is in two-way communication with each ofthe training module 15 and the ANN 7. Thus, in the embodiment of FIG. 4,the CPU 33 may select and control the appropriate training module 15,perhaps based upon information gathered from a user through the userinterface display 39. As described above, the training module 15 feedscalibration images 20 to the ANN 7 and receives feedback from the ANNregarding the output and error of the ANN 7. Likewise, the ANN may be intwo-way communication with the CPU 33. For example, once the ANN 7 istrained, the CPU 33 may feed a multi-spectral x-ray projection 5 to theANN 7 for processing, and the ANN may then produce compositioninformation 9 back to the CPU 33. The CPU 33 may further be connected touser interface display 39 which may allow a user to, for example, inputinformation about the desired composition information or output formatof the composition information, or to select an imaging device or atraining module.

The presently disclosed method and system can incorporate many x-raybased imaging technologies, such as projection imaging, tomosynthesisand computed tomography, to provide information about materialcomposition and classification. This technology may be applied in allfields that use x-ray imaging, such as medical imaging, non-destructivetesting, and security imaging. Varying methods of multi-spectral dataacquisition may also be employed. One way to acquire multi-spectra datais to take a number of x-ray projections changing the x-ray tube voltagefor each projection. This is implemented clinically in a method known asdual kV or dual energy. Another multi-spectral data acquisition methodis to acquire data with different beam filtration to change the spectralcontent. Another multi-spectral data acquisition device is aphoton-counting detector that sorts the photons by energy to give youdifferent spectral measurements. Yet another way to acquiremulti-spectral projections is using a “sandwich” detector that detectsx-rays based on depth of interaction, which results in differentdetected energy spectra for each depth. Further, these and other dataacquisition methods may be combined to get different spectrameasurements.

Any material can be used as a basis material. The inventors recognizethat virtually any material can be decomposed into an equivalentthickness, or effective contribution, of any other material or materials(unless one of the materials has an absorption K-edge). Thus,composition of an imaged object 50 can be described in terms of anequivalent basis material. For instance, in a medical imagingapplication, a location of soft tissue or bone may be equivalent tohaving a particular fraction of one basis material and another fractionof a second basis material. Those basis materials may be any materialupon which the ANN 7 is trained. Exemplary basis materials may includewater, PMMA, aluminum, polystyrene, PVC, Teflon, low-densitypolyethylene (LOPE) and even air.

Certain materials may be superior as basis materials than others. Forexample, for purposes of consistency and repeatability, materials, withconsistent, defined, and replicable compositions may be desirable. Purematerials or materials that are readily available in a consistent anddefined form may be most desirable as basis materials. Furthermore, iftwo basis materials are used in an imaging application, it may bepreferable to utilize one material with a low atomic number and a secondmaterial with a high atomic number.

FIG. 5 depicts one exemplary embodiment of a material composition output13. FIG. 5 plots simulated thicknesses of bone, soft tissue and adiposetissue in terms of equivalent thicknesses of two basis materials,polystyrene and PVC. The equivalent thickness of polystyrene incentimeters for each of the bone, adipose tissue, and soft tissue isdepicted across the x-axis 56, while the equivalent thickness incentimeters of PVC is plotted along the y-axis 58. Accordingly, thegraph of FIG. 5 provides a visual representation of exemplary basiscoefficients 45 for three types of human tissue produced by an exemplaryANN 7 trained with respect to polystyrene and PVC.

In some embodiments, an additional one or more basis materials can beadded to the training algorithm 15 applied to the ANN 7. In suchembodiments, the third material, and every additional material beyondthe third material, is preferably a material with a K-edge in the energyrange used in the imaging device, which is typically 20-140 keV (thoughit is contemplated that imaging devices may employ other energy ranges).A K-edge is a sudden increase in the attenuation coefficient of photonsoccurring at a photon energy just above the binding energy of the Kshell electron of the atoms interacting with the photons. The suddenincrease in attenuation is due to photoelectric absorption of thephotons. For this interaction to occur, the photons must have moreenergy than the binding energy of the K Shell electrons.

Use of a third (or more) K-edge material as a basis material allowsisolation of the contribution of the K-edge material. For example, anANN 7 trained using iodine as a third basis material to process CT datawould produce composition information 9 containing basis coefficients 49having non-zero values for locations in that CT scan that containiodine. The basis coefficients 49 could be used to determine the exactconcentration of iodine at those locations. For the remainder of the CTimage, i.e., for the imaged areas that do not contain iodine, the basiscoefficients would indicate the effective combination of the first andsecond basis materials and the coefficient associated with iodine wouldbe zero.

The absorption K-edge, which is preferably an energy range that you areusing for imaging, provides a unique energy signature. Two exemplaryK-edge materials used as x-ray contrast media, iodine and barium, arewell suited for absorption of diagnostic x-ray beams commonly used inmedical imaging because their K shell binding energies for absorptionare 33.2 keV and 37.4 keV, respectively, which is right in the energyrange typically employed in medical imaging. Other K-edge materials mayalso be used as basis materials, including but not limited to xenon,cesium, gadolinium (or any of the lanthanides and actinide group havingatomic number between 57 and 71), tungsten, gold (or any of the elementsin the transition metals group having an atomic number between 72 and80), thallium, or lead.

Use of a Kedge material as a basis material may be useful inapplications, such as medical imaging, where contrast agents are used.Employing the presently disclosed system and method with a K-edge basismaterial offers a way to use x-ray imaging to quantify how much agent isabsorbed by the imaged areas, such as the imaged areas of a patient.Another, similar application would be for quantitative molecular imagingwith the use of nanoparticle contrast agents.

In one medical imaging application, the disclosed method and system maybe used in imaging kidneys to distinguish between kidney stone types.The material composition of kidney stones would have unique basiscoefficients 49 that would allow a clinician to distinguish betweendifferent types of stones, which may impact the treatment plan.Likewise, the present system and method may be used in contrast imagingto distinguish between iodine (which is injected to provide contrast tothe blood) and calcified plaque in a blood vessel. In a security imagingapplication, the present system and method may be used to determine mapsof effective atomic numbers and effective density for all pixels in asecurity scan image. This may be useful, for example, for identifyingexplosives in a baggage scanner.

FIG. 6 is a system diagram of an exemplary embodiment of a system 1200for executing a method of processing x-ray images implementing an ANN1207 and a training module 1215. The system 1200 is generally acomputing system that includes a processing system 1206, storage system1204, software 1202, communication interface 1208 and a user interface1210. The processing system 1206 loads and executes software 1202 fromthe storage system 1204, including the ANN 1207 and the training module1215. When executed by the computing system 1200, training module 1215directs the processing system 1206 to operate as described in herein infurther detail, including execution of training algorithm 1217.

Although the computing system 1200 as depicted in FIG. 6 includes onesoftware module in the present example, it should be understood that oneor more modules could provide the same operation. Similarly, whiledescription as provided herein refers to a computing system 1200 and aprocessing system 1206, it is to be recognized that implementations ofsuch systems can be performed using one or more processors, which may becommunicatively connected, and such implementations are considered to bewithin the scope of the description.

The processing system 1206 can comprise a microprocessor and othercircuitry that retrieves and executes software 1202 from storage system1204. Processing system 1206 can be implemented within a singleprocessing device but can also be distributed across multiple processingdevices or sub-systems that cooperate in executing program instructions.Examples of processing system 1206 include general purpose centralprocessing units, application specific processors, and logic devices, aswell as any other type of processing device, combinations of processingdevices, or variations thereof.

The storage system 1204 can comprise any storage media readable byprocessing system 1206, and capable of storing software 1202 and/orother required data or information. The storage system 1204 can includevolatile and non-volatile, removable and non-removable media implementedin any method or technology for storage of information, such as computerreadable instructions, data structures, program modules, or other data.Storage system 1204 can be implemented as a single storage device butmay also be implemented across multiple storage devices or sub-systems.Storage system 1204 can further include additional elements, such acontroller, capable of communicating with the processing system 1206.

Examples of storage media include random access memory, read onlymemory, magnetic discs, optical discs, flash memory, virtual memory, andnon-virtual memory, magnetic sets, magnetic tape, magnetic disc storageor other magnetic storage devices, or any other medium which can be usedto storage the desired information and that may be accessed by aninstruction execution system, as well as any combination or variationthereof, or any other type of storage medium. In some implementations,the storage media can be a non-transitory storage media. In someimplementations, at least a portion of the storage media may betransitory. It should be understood that in no case is the storage mediaa propagated signal.

User interface 1210 can include any input, output, or input/outputdevices. Input devices may include a mouse, a keyboard, a voice inputdevice, a touch input device for receiving a gesture from a user, amotion input device for detecting non-touch gestures and other motionsby a user, and other comparable input devices and associated processingelements capable of receiving user input from a user. Output devicessuch as a video display or graphical display can display an interfacefurther associated with embodiments of the system and method asdisclosed herein. Speakers, printers, haptic devices and other types ofoutput devices may also be included in the user interface 1210. Asdisclosed in detail herein, the user interface 1210 operates to outputthe created object composition output 15.

As described in further detail herein, the computing system 1200receives multi-spectral x-ray projections 5 and may further receivetraining data, which may be integrated into or provided separately fromthe multi-spectral x-ray projections 5. As described above, themulti-spectral x-ray projections 5 may be any data containing spectralenergy information about the x-rays detected through the object 50. Themulti-spectral x-ray projections 5 may originate from any type of x-rayimaging device and/or detector capable of creating such multi-spectraldata.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to make and use the invention. The patentable scope of the inventionis defined by the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral languages of the claims.

1-22. (canceled)
 23. A method of processing x-ray images, the methodcomprising: training an artificial neural network to process onlymulti-spectral x-ray projections through objects whose equivalentthicknesses are desired to determine composition information in terms ofequivalent thicknesses of at least one basis material; providing amulti-spectral x-ray projection of an object whose equivalent thicknessis desired, wherein the multi-spectral x-ray projection of the objectcontains energy content information describing energy content of theobject whose equivalent thickness is desired and does not contain energycontent information describing a phantom; processing the multi-spectralx-ray projection with the artificial neural network to determinecomposition information about the object; and providing the compositioninformation about the object.
 24. The method of claim 23, whereintraining the artificial neural network includes: providing a set ofcalibration multi-spectral x-ray projections of known thicknesses ofknown objects about which composition information is to be determined asinput to the neural network, wherein the calibration multi-spectralx-ray projections only contain energy content information describing theknown objects and do not contain energy content information describing aphantom; and utilizing a training algorithm to minimize the errorbetween the determined composition information and the knownthicknesses.
 25. The method of claim 24, wherein the compositioninformation about the object includes one or more basis coefficientsmapping the object in terms of equivalent thicknesses of the at leastone basis material.
 26. The method of claim 25, wherein themulti-spectral x-ray projection of the object is a 2-dimensionalprojection and the basis coefficients comprise a 2-dimensional array ofcoefficients.
 27. The method of claim 25, wherein the multi-spectralx-ray projection of the object is a 3-dimensional computerizedtomography dataset and the basis coefficients comprise a 3-dimensionalarray of coefficients.
 28. The method of claim 23, wherein theartificial neural network is trained to determine the compositioninformation in terms of equivalent thicknesses of at least two basismaterials based only on the multi-spectral x-ray projection of theobject whose equivalent thickness is desired, and wherein thecomposition information of the object includes a set of basiscoefficients describing the object in terms of the at least two basismaterials.
 29. The method of claim 28, wherein one basis material has ahigh atomic number and one basis material has a low atomic number. 30.The method of claim 23, wherein the artificial neural network is trainedto determine the composition information in terms of equivalentthicknesses of at least three basis materials based only on themulti-spectral x-ray projection of the object whose equivalent thicknessis desired, and wherein at least one of the at least three basismaterials is a contrast agent with an absorption K-edge in an energyrange of the multi-spectral x-ray projection.
 31. The method of claim23, wherein the method further includes utilizing the compositioninformation about the object to calculate an array of coefficientsmapping the object in terms of atomic number and density.
 32. The methodof claim 23, wherein the at least one basis material includes water. 33.A system for imaging an object, the system comprising: an imaging deviceconfigured to produce a multi-spectral x-ray image of an object; and anartificial neural network trained to process the multi-spectral x-rayimage to produce composition information about an object whoseequivalent thickness is desired based on only multi-spectral x-rayprojection data containing energy content information describing theobject whose equivalent thickness is desired; wherein the neural networkis trained by providing a set of calibration x-ray projections of knownthicknesses of at least one basis material as input to the neuralnetwork and utilizing a training algorithm to minimize the error betweenthe composition information and the known thicknesses; and wherein thecomposition information includes an array of basis coefficients mappingthe object in terms of equivalent thicknesses of the at least one basismaterial.
 34. The system of claim 33, wherein the imaging deviceincludes one or more of a dual kV detector, a photon counting detector,or a dual sandwich detector.
 35. The system of claim 33, wherein thecalibration multi-spectral x-ray projections contain energy contentinformation describing objects about which composition information is tobe determined by the neural network and do not contain energy contentinformation describing a phantom.
 36. The system of claim 33, whereinthe composition information includes basis coefficients mapping theobject in terms of equivalent thicknesses of at least three basismaterials, and wherein at least one of the basis materials is a contrastagent having an absorption K-edge in an energy range of themulti-spectral x-ray projection.
 37. A method of processing x-rayimages, the method comprising: training an artificial neural network toprocess only multi-spectral x-ray projections through objects whoseequivalent thicknesses are desired to determine composition informationin terms of equivalent thicknesses of at least one basis material;wherein the training includes inputting a set of calibrationmulti-spectral x-ray projections of known thicknesses of known objectsabout which composition information is to be determined, wherein thecalibration multi-spectral x-ray projections only contain energy contentinformation describing the known objects and do not contain energycontent information describing a phantom and utilizing a trainingalgorithm to minimize the error between the determined compositioninformation and the known thicknesses; providing a multi-spectral x-rayprojection of an object whose equivalent thickness is desired, whereinthe multi-spectral x-ray projection of the object; processing themulti-spectral x-ray projection with the artificial neural network todetermine composition information about the object; and providing thecomposition information about the object.
 38. The method of claim 37,wherein the composition information includes a set of basis coefficientsmapping the object in terms of equivalent thicknesses of at least twobasis materials.
 39. The method of claim 38 wherein the artificialneural network generates a set of basis coefficients mapping the objectin terms of equivalent thicknesses of at least three basis materials,and wherein at least one of the basis materials is a contrast agenthaving an absorption K-edge in an energy range of the multi-spectralx-ray projection.
 40. The method of claim 39 where the artificial neuralnetwork is trained by providing a set of calibration x-ray projectionsof known thicknesses of the at least one basis material as input to theneural network and utilizing a training algorithm to minimize the errorbetween the composition information and the known thicknesses.
 41. Themethod of claim 37 further comprising generating an object compositionoutput to a user visually depicting a material composition of theobject.
 42. The method of claim 37, wherein the basis materials includeat least two or more of polymethyl methacrylate (PMMA), aluminum,iodine, polystyrene, PVC, Teflon, low-density polyethylene (LDPE), andwater.